This video demonstrates how multiple ground-based observation devices exchange information through communication with each other, enabling target tracking in a distributed scenario.
In recent years, decentralized sensor networks have garnered significant attention in the field of state estimation owing to enhanced robustness, scalability, and fault tolerance. Optimal fusion performance can be achieved under fully connected communication and known noise correlation structures. To mitigate communication overhead, the global state estimation problem is decomposed into local subproblems through structured observation model. This ensures that even when the communication network is not fully connected, each sensor can achieve locally optimal estimates of its observable state components. To address the degradation of fusion accuracy induced by unknown correlations in measurement noise, this paper proposes a data-driven method, termed Decentralized Information Fliter Neural Network ( DIFNet), to learn unknown noise correlations in data for discrete-time nonlinear state space models with cross-correlated measurement noises. Numerical simulations demonstrate that Decentralized IFNet achieves superior fusion performance compared to conventional filtering methods and exhibits robust characteristics in more complex scenarios, such as the presence of time-varying noise.
The fusion center refers to the processing facilities. (a) Centralized: the fusion center computes the global estimate directly from all sensor measurements $\vz$. (b) Hierarchical: low-level fusion centers compute local estimates $\hat{\vx}, \hat{\mP}$ from their own sensor measurements $\vz$, and a high-level fusion center combines these local estimates into a global estimate. (c) Decentralized: each fusion center computes a local estimate $\hat{\vx}, \hat{\mP}$ from its own measurements $\vz$, and then fuses it with estimates from neighboring centers to produce an individual fusion estimate.
Schematic diagram of the proposed DIFNet. The top part shows the generation of local estimates and error covariances by local sensors. The bottom part illustrates the communication and implementation flow at each fusion center."
RMSE of estimated position
RMSE of estimated velocity
RMSE of estimated position
RMSE of estimated velocity
RMSE of estimated position (σ = 0.5)
RMSE of estimated velocity (σ = 0.5)
* σ indicate measurement noise case